devtools::install_github("dionnecargy/SeroTrackR") # To download the package
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* preparing ‘SeroTrackR’:
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* checking for LF line-endings in source and make files and shell scripts
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* building ‘pvsero_0.3.0.tar.gz’
library(SeroTrackR) # To load the package library(tidyverse) # for data wrangling and visualisationlibrary(knitr) # for RMarkdown visualisation and PDF generation
Data Analysis: runPvSeroPipeline()
Run this global function runPvSeroPipeline() embedded within the {SeroTrackR} R package! This function contains all of the steps in order of how to perform the Plasmodium vivax serology test and treat protocol as found in our application!
Using Tutorial Dataset: Load the Data
We will be using the build-in files in the R package for this tutorial, as shown below.
This is a table containing the classification results (seropositive or seronegative) for each SampleID. In this case, the classification results are stored in the pred_class_max column as we chose the sens_spec = "maximised". If you change it to another type of threshold, then the suffix of that column will change accordingly.
You will also see the relative antibody unit (RAU) values (columns with antigen names), whether the sample passed QC check (QC_total) and the plate that they were run on.
final_analysis[[1]] %>%head() %>%kable()
SampleID
Plate
QC_total
EBP
LF005
LF010
LF016
MSP8
PTEX150
PvCSS
RBP2b.P87
pred_class_max
ABC013
plate1
pass
0.0003339
0.0015045
0.0002163
0.0014567
0.0000195
0.0001591
0.0000772
0.0003714
seropositive
ABC097
plate2
pass
0.0004324
0.0015615
0.0001944
0.0013373
0.0000195
0.0001549
0.0000705
0.0009189
seropositive
ABC181
plate3
pass
0.0003822
0.0015832
0.0002144
0.0013711
0.0000195
0.0001582
0.0000710
0.0002070
seropositive
ABC022
plate1
pass
0.0200000
0.0200000
0.0007373
0.0194885
0.0006247
0.0003145
0.0006008
0.0004895
seropositive
ABC106
plate2
pass
0.0057123
0.0193731
0.0007458
0.0195240
0.0006263
0.0003077
0.0006480
0.0126203
seropositive
ABC190
plate3
pass
0.0098260
0.0200000
0.0007400
0.0200000
0.0006020
0.0003171
0.0006555
0.0175253
seropositive
Standard Curve Plot
The standard curve plots are generated from the antibody data from the standards you indicated in your plate layout (e.g. S1-S10) and Median Fluorescent Intensity (MFI) units are displayed in log10-scale. In the case of the PvSeroTaT multi-antigen panel, the antigens will be displayed and in general your standard curves should look relatively linear (only when the y-axis is on logarithmic scale).
final_analysis[[2]]
Bead Counts QC Plot
A summary of the bead counts for each plate well are displayed, with blue indicating there are sufficient beads (≥15) or red when there are not enough. If any of the wells are red, they should be double-checked manually and re-run on a new plate if required.
The function will inform you whether there are “No repeats necessary” or provide a list of samples to be re-run. In the example data, the beads in plate 2 wells A1 and A2 will need to be repeated
The Median Fluorescent Intensity (MFI) units for each antigen is displayed for your blank samples. In general, each blank sample should have ≤50 MFI for each antigen, if they are higher they should be cross-checked manually.
In the example data, blank samples recorded higher MFI values for LF005 on plate 1 and should be checked to confirm this is expected from the assay.
final_analysis[[5]]
Model Output Plot
The automated data processing in this app allows you to convert your Median Fluorescent Intensity (MFI) data into Relative Antibody Units (RAU) by fitting a 5-parameter logistic function to the standard curve on a per-antigen level. The results from this log-log conversion should look relatively linear for each antigen.
final_analysis[[6]]
$plate1
$plate2
$plate3
Run Classification: No
no_classification_final_analysis <-runPvSeroPipeline(raw_data = your_raw_data, plate_layout = your_plate_layout, platform ="magpix", location ="ETH", experiment_name ="experiment1", classify ="No", ########################## key if you do NOT want any classification performed i.e., you do not have PvSeroTaT antigens algorithm_type ="antibody_model", sens_spec ="maximised")
Firstly, we will be using our example data that’s in-built in the package. Here replace the system.file() argument with the file path for your package.
Caitlin and Dionne have worked on a function to (a) process raw Serological data and (b) convert MFI to RAU. The runPlasmoSero5point() function will output three data frames: (i) Results from MAGPIX (MFI), (ii), Counts for each sample, (iii) Processed RAU values for each sample. These dataframes can be used for further analysis and can be transformed as you wish.
pk_analysis_1 <-runPlasmoSero5point(raw_data = your_raw_data_5std, platform ="magpix", plate_layout = your_plate_layout_5std, date ="2025-07-30"# Optional: This will default to today's date)
Standard Curve Plot: One Curve
As requested, this is a plot of ONE standard curve which you will specify. You can modify this plot as you see fit by piping other ggplot2() arguments.
plotOneStdCurve(pk_analysis_1, "plate1")
Standard Curve Plot: Compare Two Curves
This function allows you to plot all of your standard curves.
plotManyStdCurves(pk_analysis_1)
10-Point Standard Curve
These steps are very similar to the 5-point standard curve, except here we use the runPlasmoSero10point() function.
pk_analysis_2 <-runPlasmoSero10point(raw_data = your_raw_data_10std, platform ="magpix", plate_layout = your_plate_layout_10std, date ="2025-07-30"# Optional: This will default to today's date)
Standard Curve Plot: One Curve
plotOneStdCurve(pk_analysis_2, "plate1")
Standard Curve Plot: Compare Two Curves
plotManyStdCurves(pk_analysis_2)
Visualisation of the {SeroTrackR} R Package
We have used the {targets} R package to generate a pipeline! This allows us to:
Automatically detect the dependencies of each step
One-command execution
Automatic caching
Automatic detection of changes in data and/or code
For more information on {targets} see this tutorial.
here() starts at /Users/Dionne/Documents/GitHub/SeroTrackR
✔ skipped pipeline [42ms, 15 skipped]
Warning message:
package ‘targets’ was built under R version 4.4.1
here() starts at /Users/Dionne/Documents/GitHub/SeroTrackR
Warning message:
package ‘targets’ was built under R version 4.4.1
